Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

Mavrovouniotis, M ORCID: https://orcid.org/0000-0002-5281-4175, Müller, FM and Yang, S, 2016. Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems. IEEE Transactions on Cybernetics. ISSN 2168-2267

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Abstract

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Cybernetics
Creators:	Mavrovouniotis, M., Müller, F.M. and Yang, S.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	13 June 2016
ISSN:	2168-2267
Identifiers:	Number Type 10.1109/TCYB.2016.2556742 DOI 27323387 PubMed ID
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	01 Dec 2016 10:57
Last Modified:	09 Jun 2017 14:08
URI:	https://irep.ntu.ac.uk/id/eprint/29206

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